论文标题
优化的管道，用于大鼠大脑的功能连通性分析
An optimized pipeline for functional connectivity analysis in the rat brain
论文作者
论文摘要
静止状态功能MRI（RS-FMRI）是研究功能连通性和脑部疾病的广泛而有力的工具。但是，功能连通性分析可能会受到非神经来源（例如生理学）的随机和结构化噪声的严重影响。因此，首先减少热噪声，然后通过优化的数据处理方法正确识别和删除RS-FMRI信号中的非神经伪影。但是，现有针对这些影响的现有工具已为人脑开发，并且不容易转座到大鼠数据。因此，本研究的目的是建立一个数据处理管道，该管道可以从大鼠RS-FMRI数据中稳健地消除随机和结构化的噪声。它包括一种基于Marchenko-Pastur原理分析（MP-PCA）方法，FMRIB的基于ICA的基于ICA的Xnoiseifier（FIX）的新型DeNoising方法，用于自动人工伪像分类和清洁以及全局信号回归。我们的结果表明：i）MP-PCA降级大大提高了时间信号噪声比率； ii）预先训练的修复分类器在人工制品分类方面具有很高的精度； iii）与对照组相比，伪影清洁和全球信号回归是最大程度地减少对照动物内部变异性并确定功能连通性变化的重要步骤。
Resting state functional MRI (rs-fMRI) is a widespread and powerful tool for investigating functional connectivity and brain disorders. However, functional connectivity analysis can be seriously affected by random and structured noise from non-neural sources such as physiology. Thus, it is essential to first reduce thermal noise and then correctly identify and remove non-neural artefacts from rs-fMRI signals through optimized data processing methods. However, existing tools that correct for these effects have been developed for human brain and are not readily transposable to rat data. Therefore, the aim of the present study was to establish a data processing pipeline that can robustly remove random and structured noise from rat rs-fMRI data. It includes a novel denoising approach based on the Marchenko-Pastur Principle Component Analysis (MP-PCA) method, FMRIB's ICA-based Xnoiseifier (FIX) for automatic artefact classification and cleaning, and global signal regression. Our results show that: I) MP-PCA denoising substantially improves the temporal signal-to-noise ratio; II) the pre-trained FIX classifier achieves a high accuracy in artefact classification; III) both artefact cleaning and global signal regression are essential steps in minimizing the within-group variability in control animals and identifying functional connectivity changes in a rat model of sporadic Alzheimer's disease, as compared to controls.
